Lead Analyst, Product Innovation

Location Ohio
Discipline: Machine Learning
Contact name: Tom Goldberg

Contact email: tom@enigma-rec.ai
Job ref: 34785
Published: 29 days ago

We are hiring for a prominent provider of revenue cycle management services for healthcare facilities, including hospitals and physician practices. It oversees billions in net revenue and partners with a wide array of health systems to deliver comprehensive financial solutions through managed service partnerships. The organization assists providers in optimizing their technology and implements advanced solutions tailored to their specific needs.

Role Overview:
The Lead Analyst in Product Innovation is responsible for analyzing operational and transactional data to identify improvement opportunities within the revenue cycle management process. This role involves collaboration with cross-functional teams, including operations and technology groups, to develop business strategies and implement solutions that enhance revenue cycle performance. The insights and recommendations provided will assist in optimizing revenue streams and reducing costs for clients.

Responsibilities:

  • Conduct thorough analyses of healthcare revenue cycle processes, including patient registration, prior authorization, billing, coding, claims processing, payment posting, and accounts receivable follow-up.

  • Identify inefficiencies within the revenue cycle and develop data-driven recommendations for process improvements.

  • Collaborate with stakeholders to brainstorm opportunities, gather, and document business requirements.

  • Perform in-depth data analysis to uncover trends, patterns, and opportunities for revenue optimization and cost reduction.

  • Prepare and present executive summaries to stakeholders, conveying complex data in a clear and actionable manner.

  • Develop performance metrics and key performance indicators (KPIs) to track revenue cycle performance and measure the effectiveness of proposed solutions.

  • Work with other technology teams to design, implement, and test technology solutions that enhance revenue cycle operations.

  • Ensure compliance with regulatory and reimbursement policies, adhering to industry standards and best practices.

  • Stay current with industry trends, emerging technologies, and regulatory changes affecting revenue cycle management and share knowledge proactively with the team.

  • Mentor junior analysts as the team expands.

Requirements:

  • Required Certifications: Relevant certifications related to the role are necessary.

  • Desired Work Experience:

    • 7 to 10 years of experience in product innovation or a similar role within the healthcare revenue cycle management sector.

  • Preferred Education:

    • Master’s Degree or equivalent experience in Finance, Accounting, or related fields is preferred.

    • A Bachelor’s degree in Business Administration, Finance, Computer Science, Mathematics, Engineering, Healthcare Management, or a related field is required.

  • Preferred Knowledge, Skills, and Abilities:

    • Proven experience in product innovation or similar roles.

    • Strong understanding of healthcare revenue cycle processes, including billing, coding, claims processing, and reimbursement methodologies.

    • Proficiency in data analysis tools and techniques, such as SQL, Excel, or statistical software.

    • Familiarity with healthcare IT systems, including EHRs, practice management systems, and billing software.

    • Excellent analytical skills to interpret complex data sets and derive meaningful insights.

    • Strong problem-solving abilities with a focus on identifying root causes and proposing effective solutions.

    • Exceptional written and verbal communication skills, with the ability to present complex information to various stakeholders.

    • Detail-oriented and highly organized, capable of managing multiple projects and priorities simultaneously.

    • Knowledge of healthcare regulations, compliance standards, and industry trends, including HIPAA and value-based reimbursement models.